#!/usr/bin/env python3

""" Convolutional Neural Network module."""


# built-ins

# site-packages
import numpy
import theano

from theano import tensor as T
from theano.tensor.nnet import conv
from theano.tensor.signal import downsample

# local-packages



class LeNetConvPoolLayer(object):
    """Pool Layer of a convolutional network."""

    def __init__(self, rng, input, filter_shape, image_shape, poolsize=(2, 2)):
        """
        Allocate a LeNetConvPoolLayer with shared variable internal parameters.

        :type rng: numpy.random.RandomState
        :param rng: a random number generator used to initialize weights

        :type input: theano.tensor.dtensor4
        :param input: symbolic image tensor, of shape image_shape

        :type filter_shape: tuple or list of length 4
        :param filter_shape: (number of filters, num input feature maps `D2`,
                              filter height `H2`, filter width `W2`)

        :type image_shape: tuple or list of length 4
        :param image_shape: (batch size `N`, num input feature maps `D1`,
                             image height `H1`, image width `W1`)

        :type poolsize: tuple or list of length 2
        :param poolsize: the downsampling (pooling) factor (#rows, #cols)
        """

        assert image_shape[1] == filter_shape[1]
        self.input = input

        # there are "num input feature maps * filter height * filter width"
        # inputs to each hidden unit
        fan_in = numpy.prod(filter_shape[1:])
        # each unit in the lower layer receives a gradient from:
        # "num output feature maps * filter height * filter width" /
        #   pooling size
        fan_out = (filter_shape[0] * numpy.prod(filter_shape[2:]) /
                   numpy.prod(poolsize))
        # initialize weights with random weights
        W_bound = numpy.sqrt(6. / (fan_in + fan_out))
        self.W = theano.shared(
            numpy.asarray(
                rng.uniform(low=-W_bound, high=W_bound, size=filter_shape),
                dtype=theano.config.floatX),
            borrow=True)

        # the bias is a 1D tensor -- one bias per output feature map
        b_values = numpy.zeros((filter_shape[0],), dtype=theano.config.floatX)
        self.b = theano.shared(value=b_values, borrow=True)

        # convolve input feature maps with filters
        conv_out = conv.conv2d(
            input=input,
            filters=self.W,
            filter_shape=filter_shape,
            image_shape=image_shape)

        # downsample each feature map individually, using maxpooling
        pooled_out = downsample.max_pool_2d(
            input=conv_out,
            ds=poolsize,
            ignore_border=True)

        # add the bias term. Since the bias is a vector (1D array), we first
        # reshape it to a tensor of shape (1, n_filters, 1, 1). Each bias will
        # thus be broadcasted across mini-batches and feature map
        # width & height
        self.output = T.tanh(pooled_out + self.b.dimshuffle('x', 0, 'x', 'x'))

        # store parameters of this layer
        self.params = [self.W, self.b]

        # keep track of model input
        self.input = input


# def test_cnn():
#     x = T.matrix('x')   # the data is presented as rasterized images
#     y = T.ivector('y')  # the labels are presented as 1D vector of
#                         # [int] labels

#     ######################
#     # BUILD ACTUAL MODEL #
#     ######################
#     print '... building the model'

#     # Reshape matrix of rasterized images of shape (batch_size, 28 * 28)
#     # to a 4D tensor, compatible with our LeNetConvPoolLayer
#     # (28, 28) is the size of MNIST images.
#     layer0_input = x.reshape((batch_size, 1, 28, 28))

#     # Construct the first convolutional pooling layer:
#     # filtering reduces the image size to (28-5+1 , 28-5+1) = (24, 24)
#     # maxpooling reduces this further to (24/2, 24/2) = (12, 12)
#     # 4D output tensor is thus of shape (batch_size, nkerns[0], 12, 12)
#     layer0 = LeNetConvPoolLayer(
#         rng,
#         input=layer0_input,
#         image_shape=(batch_size, 1, 28, 28),
#         filter_shape=(nkerns[0], 1, 5, 5),
#         poolsize=(2, 2)
#     )

#     # Construct the second convolutional pooling layer
#     # filtering reduces the image size to (12-5+1, 12-5+1) = (8, 8)
#     # maxpooling reduces this further to (8/2, 8/2) = (4, 4)
#     # 4D output tensor is thus of shape (batch_size, nkerns[1], 4, 4)
#     layer1 = LeNetConvPoolLayer(
#         rng,
#         input=layer0.output,
#         image_shape=(batch_size, nkerns[0], 12, 12),
#         filter_shape=(nkerns[1], nkerns[0], 5, 5),
#         poolsize=(2, 2)
#     )

#     # the HiddenLayer being fully-connected, it operates on 2D matrices of
#     # shape (batch_size, num_pixels) (i.e matrix of rasterized images).
#     # This will generate a matrix of shape (batch_size, nkerns[1] * 4 * 4),
#     # or (500, 50 * 4 * 4) = (500, 800) with the default values.
#     layer2_input = layer1.output.flatten(2)

#     # construct a fully-connected sigmoidal layer
#     layer2 = HiddenLayer(
#         rng,
#         input=layer2_input,
#         n_in=nkerns[1] * 4 * 4,
#         n_out=500,
#         activation=T.tanh
#     )

#     # classify the values of the fully-connected sigmoidal layer
#     layer3 = LogisticRegression(input=layer2.output, n_in=500, n_out=10)

#     # the cost we minimize during training is the NLL of the model
#     cost = layer3.negative_log_likelihood(y)

#     # create a function to compute the mistakes that are made by the model
#     test_model = theano.function(
#         [index],
#         layer3.errors(y),
#         givens={
#             x: test_set_x[index * batch_size: (index + 1) * batch_size],
#             y: test_set_y[index * batch_size: (index + 1) * batch_size]
#         }
#     )

#     validate_model = theano.function(
#         [index],
#         layer3.errors(y),
#         givens={
#             x: valid_set_x[index * batch_size: (index + 1) * batch_size],
#             y: valid_set_y[index * batch_size: (index + 1) * batch_size]
#         }
#     )

#     # create a list of all model parameters to be fit by gradient descent
#     params = layer3.params + layer2.params + layer1.params + layer0.params

#     # create a list of gradients for all model parameters
#     grads = T.grad(cost, params)

#     # train_model is a function that updates the model parameters by
#     # SGD Since this model has many parameters, it would be tedious to
#     # manually create an update rule for each model parameter. We thus
#     # create the updates list by automatically looping over all
#     # (params[i], grads[i]) pairs.
#     updates = [
#         (param_i, param_i - learning_rate * grad_i)
#         for param_i, grad_i in zip(params, grads)
#     ]

#     train_model = theano.function(
#         [index],
#         cost,
#         updates=updates,
#         givens={
#             x: train_set_x[index * batch_size: (index + 1) * batch_size],
#             y: train_set_y[index * batch_size: (index + 1) * batch_size]
#         }
#     )
